This paper presents a new discriminative model called Local Deep Neural Network (Local-DNN), which is based on two key concepts: local features and deep architectures. This model learns to classify small patches extracted from images using a standard DNN. The fi-nal classification of each image is performed using a simple voting scheme that takes into account the contributions from all the patches of that image. The experiments carried out have evaluated the model on the gender recognition problem using unconstrained face images, by following two benchmarks proposed for the LFW and the Gallagher datasets.